1. Field of the Invention
The present invention relates to a walking pace estimation device and method, and more particularly to a device and method for estimating an adaptive pace depending on users with different paces.
2. Description of the Related Art
In general, walking pace estimation can be detected through an accelerometer that shows a change in acceleration for an impact transmitted whenever a person with a terminal having an attached sensor capable of measuring acceleration takes a step. In order to detect a precise step, the change in acceleration should be precisely measured. A process for linearly combining output values of an acceleration sensor to detect acceleration changes is generally used to estimate a pace.
A process currently used for pace estimation uses a neural network instructed in a pace with a specific velocity. A substantial error rate for a pace with a specific velocity does not occur when the neural network has been instructed. However, a large error rate for a pace with a specific velocity can occur when the neural network has not been instructed. Since the pace of a person varies, it is likely that an error will occur when the process has been instructed in one pace and is applied as described above.
In order to examine whether a neural network can adapt to a user's walking pattern when it has not been instructed, an experimental value for a specified pace will be discussed through a second experimenter different from a first experimenter having instructed the neural network off line. It is assumed that the second experimenter has physical characteristics and a walking behavior different from the first experimenter. The second experimenter produces two sets for each group, the sets being classified into fast, normal and slow paces, i.e., for a total of six data sets. Such a data set is composed of an acceleration dispersion and a walking frequency for each step. When errors generated in a case where data of the second experimenter is applied to the neural network having been instructed in the pace of the first experimenter are applied to the six data sets, errors can be represented as shown in Table 1.
TABLE 1FastNormalSlowFastNormalSlowPace 1Pace 1Pace 1Pace 2Pace 2Pace 2Error7.486.060.127.128.604.43Percentage(%)
Since there occurs an error of about 10% when estimating a user's pace when the neural network has not been instructed, it can be seen that an instruction is required for the purpose of estimating a precise pace. If the neural network is renewed whenever a Global Positioning System (GPS) signal is received due to the amplitude of a walking frequency of about 3 Hz when GPS information is calculated from a GPS signal received at 1 Hz, data for instructing can be obtained. A condition is set so this data is immediately used in the instruction of the neural network. One set of fast, normal and slow paces in the six data sets produced by the second experimenter are used in an online instruction, and errors are obtained with the rest of the sets whenever transmission intensity is renewed each time. If the number of updated data is changed, changes in error can appear as shown in FIGS. 1 to 3. Here, the x-axis denotes the sequential number of updates and the y-axis denotes the size of an error. The graphs of FIGS. 1 to 3 show changes in error for the respective data sets of fast, normal and slow paces, which are used in verification. As can be seen through the graphs of FIGS. 1 to 3, in a case where the respective data sets are sequentially used in instruction, they are renewed with transmission intensity suitable for data only in their own instruction areas.
That is, when the neural network has been instructed in the fast pace, it outputs a precise pace only for the acceleration dispersion and walking frequency of the fast pace data set. Similarly, when the neural network has been instructed in the normal or slow pace, it outputs a precise pace only for the acceleration dispersion and walking frequency of the normal or slow pace data set.
As described above, individuals normally walk with paces falling within a narrow pace area. They continue to step fast in a condition of stepping fast, and continue to step slow in a condition of stepping slow. Thus, in a case where a neural network having been instructed in a pace with a specific velocity is used, there is a problem in that substantial pace errors can occur in an applied condition where instructions regarding the pace with an instructed velocity are not performed. Further, a pace estimation using a GPS receiver is possible in a position where a GPS signal can be received, while it is impossible in a position where a GPS signal cannot be received.